{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "50dc63aa-68cb-48d7-a83a-7474b2eea7cb",
   "metadata": {},
   "source": [
    "# Goals\n",
    "The purpose of the notebook is to create two annual time series that describe the number of international congresses:\n",
    "1. hosted in a given country \n",
    "1. occurring worldwide\n",
    "\n",
    "To create these series, this notebook leverages both volumes of the following source: \n",
    "* Union of International Associations (UIA). 1960. _Les congrès internationaux, liste complète. International congresses, full list_ [worldcat](https://worldcat.org/title/23419582)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32eabb6-cb21-45ac-abac-e81a5ea5ea92",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac29ec7d-82a5-4797-9d58-3c995f2b6fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv, re, requests, pycountry, os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7ea316e-c0ab-4421-a331-d15f36c7d373",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "plt.rcParams['font.sans-serif'] = ['Arial']\n",
    "plt.rcParams[\"font.style\"] = \"normal\"\n",
    "plt.rcParams[\"axes.labelcolor\"] = \"gray\"\n",
    "plt.rcParams[\"text.color\"] = \"grey\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d78ec2-efe6-4816-8d9b-246196f032d7",
   "metadata": {},
   "source": [
    "Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be44c262-7027-4d77-8cf6-2cdf1c66a18e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/GoogleDrive-107745440581041782819/My Drive/00_Researching/16_SocialScientization/-04_EJS/00_replication/\n",
      "/Volumes/GoogleDrive-107745440581041782819/My Drive/00_Researching/16_SocialScientization/-04_EJS/00_replication/00_data/01_ivs/\n"
     ]
    }
   ],
   "source": [
    "DIRECTORY = os.path.dirname(os.getcwd()) + '/'\n",
    "print(DIRECTORY)\n",
    "IVS = DIRECTORY + '00_data/01_ivs/'\n",
    "print(IVS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eada9360-56c3-42d6-939e-8adf3727bf18",
   "metadata": {},
   "source": [
    "# Clean OCRed PDFs\n",
    "\n",
    "I first used `ocrmypdf` from the command line to OCR both the English and French in the scanned PDFs. Then, I used the `tabla`'s GUI to structure the pages into tables and export as a csv. In what follows, I clean up the data in a first pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da4a9b69-006a-4f8a-a96a-11f08ce90b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "til99 = \"UIA_congresses1899.csv\"\n",
    "til14 = \"UIA_congresses1914.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d957ebc6-94be-4176-8536-a936c87e37dd",
   "metadata": {},
   "source": [
    "## 1800-1889"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d636ec6f-5793-4198-bf5b-27af38287112",
   "metadata": {},
   "outputs": [],
   "source": [
    "months =   ['janv.',\n",
    "            'févri.',\n",
    "            'mars',\n",
    "            'avr.',\n",
    "            'mai',\n",
    "            'juin',\n",
    "            'juil.',\n",
    "            'août',\n",
    "            'aout',\n",
    "            'sept.',\n",
    "            'oct.',\n",
    "            'nov.',\n",
    "            'déc.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23575aa5-97bb-49bb-af1d-ccf26ad63978",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open (IVS+til99, \"r\") as f:\n",
    "    csv_reader = csv.reader(f)\n",
    "    # ignore top two rows, pre 1800s\n",
    "    next(csv_reader)\n",
    "    next(csv_reader)\n",
    "    year = \"\"\n",
    "    years = {}\n",
    "    for index, row in enumerate(csv_reader):\n",
    "        broken = \"\"\n",
    "        line = \" \".join(row).strip()\n",
    "        no = \"\"\n",
    "        # print(line)\n",
    "        line_tkns = line.split()\n",
    "        if len(line_tkns) > 0:\n",
    "            # grab year \n",
    "            if len(line_tkns[0]) == 4:\n",
    "                if line_tkns[0].isdigit() and eval(line_tkns[0]) in range(1800,1914):\n",
    "                    year = line_tkns[0]\n",
    "                    if year not in years:\n",
    "                        years[year] = []\n",
    "                    # print(year)\n",
    "            # grab broken line\n",
    "            if line_tkns[0].islower():                          # NOT CAPTURING DATES ON LINE 2\n",
    "                broken += \" \".join(line_tkns[0:])\n",
    "                matches = re.findall(r'\\d+', broken)\n",
    "                if len(matches) > 0:\n",
    "                    broken = broken.split(matches[0])[0].strip()\n",
    "            # if year not in line_tkns:\n",
    "            if len(line_tkns) > 1:\n",
    "                if len(broken) == 0:\n",
    "                    if line_tkns[0].isdigit():\n",
    "                        no = line_tkns[0] + \" \"\n",
    "                        line_cln = \" \".join(line_tkns[1:])\n",
    "                    else: \n",
    "                        no = \" \"\n",
    "                        line_cln = \" \".join(line_tkns)\n",
    "                    # print(line_cln)\n",
    "                    if line_cln[0].isdigit():\n",
    "                        line_cln_again = [\"<nTH>\"]\n",
    "                        line_cln_again.extend(line_cln.split()[1:])\n",
    "                        line_cln = \" \".join(line_cln_again)               \n",
    "                    matches = re.findall(r'\\d+', line_cln)      # CAPTURING CONF. ITERATION\n",
    "                    if len(matches) > 0:\n",
    "                        name_place = line_cln.split(matches[0])[0].strip()\n",
    "                        # print(name_place)\n",
    "                    else:\n",
    "                        name_place = line_cln\n",
    "                    years[year].extend([str(no) + name_place])\n",
    "                else: \n",
    "                    if \"-\" in years[year][-1]:\n",
    "                        years[year][-1] = years[year][-1][:-1]\n",
    "                        years[year][-1] += \" \" + broken\n",
    "                    else: \n",
    "                        years[year][-1] += \" \" + broken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b41fbe0f-07db-4939-9878-688f71ff5cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(IVS+\"UIA_congresses1899_quick_clean.csv\", 'w') as f: \n",
    "    csv_writer = csv.writer(f)\n",
    "    csv_writer.writerow([\"year\", \"congress\"])\n",
    "    for year, congresses in years.items():\n",
    "        for congress in congresses: \n",
    "            csv_writer.writerow([year, congress])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4069c00-aeca-4d5e-aa94-710d42f35a21",
   "metadata": {},
   "source": [
    "## 1890-1914"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c27a6f9c-82de-4d4a-90e9-28de01067c14",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open (IVS+til14, \"r\") as f:\n",
    "    csv_reader = csv.reader(f)\n",
    "    year = \"\"\n",
    "    years = {}\n",
    "    for index, row in enumerate(csv_reader):\n",
    "        broken = \"\"\n",
    "        line = \" \".join(row).strip()\n",
    "        no = \"\"\n",
    "        # print(line)\n",
    "        line_tkns = line.split()\n",
    "        if len(line_tkns) > 0:\n",
    "            # grab year \n",
    "            if len(line_tkns) == 1:\n",
    "                if line_tkns[0].isdigit() and eval(line_tkns[0]) in range(1900,1915):\n",
    "                    # print(line_tkns[0])\n",
    "                    year = line_tkns[0]\n",
    "                    # print(year)\n",
    "                    if year not in years:\n",
    "                        years[year] = []\n",
    "            # grab broken line\n",
    "            if line_tkns[0].islower():\n",
    "                broken += \" \".join(line_tkns[0:])\n",
    "                matches = re.findall(r'\\d+', broken)\n",
    "                if len(matches) > 0:\n",
    "                    broken = broken.split(matches[0])[0].strip()\n",
    "                # print(broken)\n",
    "            if len(line_tkns) > 1: \n",
    "                if len(broken) == 0:\n",
    "                    if line_tkns[0].isdigit():\n",
    "                        no = line_tkns[0] + \" \"\n",
    "                        line_cln = \" \".join(line_tkns[1:])\n",
    "                    else: \n",
    "                        no = \" \"\n",
    "                        line_cln = \" \".join(line_tkns)\n",
    "                    if line_cln[0].isdigit():\n",
    "                        line_cln_again = [\"<nTH>\"]\n",
    "                        line_cln_again.extend(line_cln.split()[1:])\n",
    "                        line_cln = \" \".join(line_cln_again)\n",
    "                    # print(line_cln)\n",
    "                    matches = re.findall(r'\\d+', line_cln)  \n",
    "                    # print(matches, line_cln)\n",
    "                    if len(matches) > 0:\n",
    "                        name_place = line_cln.split(matches[0])[0].strip()\n",
    "                        # print(name_place)\n",
    "                    else:\n",
    "                        name_place = line_cln\n",
    "                    years[year].extend([str(no) + name_place])\n",
    "                    # print(name_place)    \n",
    "                else: \n",
    "                    if len(years[year]) > 0: \n",
    "                        if \"-\" in years[year][-1]:\n",
    "                            years[year][-1] = years[year][-1][:-1]\n",
    "                            years[year][-1] += \" \" + broken\n",
    "                            # print(lines[year][-1])\n",
    "                        else: \n",
    "                            years[year][-1] += \" \" + broken\n",
    "                            # print(lines[year][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45bbf362-6b84-479b-88dc-06bd8831ca59",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(IVS+\"UIA_congresses1914_quick_clean.csv\", 'w') as f: \n",
    "    csv_writer = csv.writer(f)\n",
    "    csv_writer.writerow([\"year\", \"congress\"])\n",
    "    for year, congresses in years.items():\n",
    "        for congress in congresses: \n",
    "            csv_writer.writerow([year, congress])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e3b5a1-0649-4ace-a7ac-4ee0624715eb",
   "metadata": {},
   "source": [
    "## Manually clean data\n",
    "* The data outputted above is still messy: conference indices are mis-recognized, dates are still in the conference/congress name, and some rows are even missed altogether. *\n",
    "* So, by hand, I cross-referenced the CSV with the PDF of the original source and I fixed any incongruencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f72351d4-39ff-455b-8775-98502774cd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "congresses99 = pd.read_csv(IVS+\"UIA_congresses1899_manual_clean.csv\")\n",
    "congresses14 = pd.read_csv(IVS+\"UIA_congresses1914_manual_clean.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5488d60d-76ac-4922-86c0-a6ff1a9e5ef3",
   "metadata": {},
   "source": [
    "## Find country host of conferences\n",
    "To do this, we'll follow these steps: \n",
    "1. Look up city's alpha2 code using geonames\n",
    "1. Convert alpha2 code to alpha3 code using pycountry\n",
    "1. Look up VDEM country name of alpha3 codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6654e5af-9d3a-4d7d-a59f-18d9921bfd3a",
   "metadata": {},
   "source": [
    "### Grab Congress ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "618525be-c2bd-4296-9d69-ec779a2da8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "congresses99[\"id\"] = congresses99['congress'].apply(lambda x: x.split()[0].strip())\n",
    "congresses14[\"id\"] = congresses14['congress'].apply(lambda x: x.split()[0].strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bb5e79-5356-4445-a1c1-fdc57067ded2",
   "metadata": {},
   "source": [
    "### Grab City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c28efff9-3095-4312-954e-46368c0b4873",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_city(s):\n",
    "    city = s.split()[-1]\n",
    "    if city in months:\n",
    "        city = s.split()[-2]\n",
    "    else: \n",
    "        return city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e09b396c-528e-4bbe-809d-291d3267fcc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "congresses99['city'] = congresses99['congress'].apply(get_city)\n",
    "congresses14['city'] = congresses14['congress'].apply(get_city)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae0a3af-b61f-405a-91cb-ac14de98da94",
   "metadata": {},
   "source": [
    "### Create dict of all observed cities\n",
    "This way, we don't inefficiently look up a city hundreds of times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8441e91b-8550-48d0-9024-d48fd85e8fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dict from pandas col, with cities as keys\n",
    "cities99 = dict.fromkeys(congresses99['city'])\n",
    "cities14 = dict.fromkeys(congresses14['city'])\n",
    "\n",
    "# Drop None keys\n",
    "cities99.pop(None)\n",
    "cities14.pop(None)\n",
    "\n",
    "# Drop misrecognized \n",
    "cities99.pop(\"le\")\n",
    "\n",
    "# Combine for whole century \n",
    "all_cities = list(cities99.keys())\n",
    "all_cities.extend(list(cities99.keys()))\n",
    "all_cities = dict.fromkeys(all_cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e992c9-e33e-4cf4-b934-e301be614429",
   "metadata": {},
   "source": [
    "### Create dictionary of VDEM country names and alpha3 codes.\n",
    "Since VDEM has all the country-year data, we'll use its country names for the cities>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9b5b62b-88cd-414a-9df0-fab4713f383a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in VDEm countries and alpha3 codes\n",
    "vdem_countries = pd.read_csv(IVS+\"vdem10.csv\",\n",
    "                        usecols=[\"country_name\", \"country_text_id\"])\n",
    "\n",
    "# Drop duplicates\n",
    "vdem_countries = vdem_countries.drop_duplicates()\n",
    "\n",
    "# Convert into a dict\n",
    "vdem_countries = dict(zip(vdem_countries['country_text_id'], vdem_countries[\"country_name\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95159f2f-feb9-4f8e-ab0b-e27de68df47e",
   "metadata": {},
   "source": [
    "### Look up the country of each city\n",
    "We'll put the each city's country as its value in the `all_cities` dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a142c5e-f0ae-4bea-8a65-ac8497c69be4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monaco MCO\n"
     ]
    }
   ],
   "source": [
    "for city in all_cities:\n",
    "    # look up city using geooname advance search\n",
    "    # featureClass \"P\" means city\n",
    "    response = requests.request(\"GET\", f\"https://www.geonames.org/advanced-search.html?q={city}&country=&featureClass=P\")\n",
    "    try:\n",
    "        # geonames gives just 2-letter name; \n",
    "        # convert to 3-letter abbrev to link to VDEM states\n",
    "        country_alpha2 = re.findall(\"/countries.*\\.html\", response.text)[0].split(\".\")[0].split(\"/\")[2]\n",
    "        country_alpha3 = pycountry.countries.get(alpha_2=country_alpha2).alpha_3\n",
    "        all_cities[city] = vdem_countries[country_alpha3]\n",
    "    except: \n",
    "        try:\n",
    "            # look up city generically, since a few confs/congs only report country\n",
    "            reponse = response = requests.request(\"GET\", f\"https://www.geonames.org/search.html?q={city}\")\n",
    "            country_alpha2 = re.findall(\"/countries.*\\.html\", response.text)[0].split(\".\")[0].split(\"/\")[2]\n",
    "            country_alpha3 = pycountry.countries.get(alpha_2=country_alpha2).alpha_3\n",
    "            all_cities[city] = vdem_countries[country_alpha3]\n",
    "        except:\n",
    "            # give up; report weird countries\n",
    "            print(city, country_alpha3)\n",
    "            all_cities[city] = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7858de6-d1f3-4df8-bfd5-ceaf35e88020",
   "metadata": {},
   "source": [
    "### Retrieve country of each conference based on its city\n",
    "We'll create a new column for the country. It will be list-wise populated by returning the country of the city that appears in the city column using the `all_cities` dict we created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dbcd66c8-249d-4f47-a40f-c4f89ee12422",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use dict to generate key in country in df\n",
    "congresses99['state'] = congresses99['city'].apply(lambda x: all_cities[x] if x in all_cities else None)\n",
    "congresses14['state'] = congresses14['city'].apply(lambda x: all_cities[x] if x in all_cities else None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d81632a-d3ef-412f-82a4-375921ca4c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>congress</th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1900</td>\n",
       "      <td>1 Cng I de la marine marchande Paris</td>\n",
       "      <td>1</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1900</td>\n",
       "      <td>2 Cng I des cuisiniers Stuttgart</td>\n",
       "      <td>2</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1900</td>\n",
       "      <td>3 &lt;nTH&gt; Cng I d’archéologie chrétienne Rome</td>\n",
       "      <td>3</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1900</td>\n",
       "      <td>4 Cnf I pour la protection des ani maux en Afr...</td>\n",
       "      <td>4</td>\n",
       "      <td>Londres</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1900</td>\n",
       "      <td>5 &lt;nTH&gt; Cng I des sciences de lécriture Paris</td>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year                                           congress id       city  \\\n",
       "0  1900               1 Cng I de la marine marchande Paris  1      Paris   \n",
       "1  1900                   2 Cng I des cuisiniers Stuttgart  2  Stuttgart   \n",
       "2  1900        3 <nTH> Cng I d’archéologie chrétienne Rome  3       Rome   \n",
       "3  1900  4 Cnf I pour la protection des ani maux en Afr...  4    Londres   \n",
       "4  1900      5 <nTH> Cng I des sciences de lécriture Paris  5      Paris   \n",
       "\n",
       "            state  \n",
       "0          France  \n",
       "1         Germany  \n",
       "2           Italy  \n",
       "3  United Kingdom  \n",
       "4          France  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "congresses14.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc15e2cc-107d-45af-9bdf-022149191058",
   "metadata": {},
   "source": [
    "# Get Subject Headings of All Congresses from Index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c29e76-fbf6-4dac-8520-5ad2e7a25847",
   "metadata": {},
   "source": [
    "## Read in index\n",
    "The index organizes conferences and congresses IDs under social scientific / reform subject headings. I created this CSV by hand. It contains the following features:\n",
    "- `heading` - str the subject headng of the conference that appears in the index\n",
    "- `til99` - list of the IDs of congs/confs, 1800–1899\n",
    "- `til14` — list the IDs of congs/congs, 1900–1914\n",
    "- `sci` — int where `1` = social science; `0` = reformist (progress-, justice-oriented initiatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ad591ac-a49e-4f7d-80bd-6c25418aa921",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_index(s):\n",
    "    # The indicies have white space, misrecongized commas\n",
    "    # and are packaged as list-like strings. This returns\n",
    "    # a cleaned version.\n",
    "    s_clean = [i.replace(\"\\n\", \"\").strip() for i in s.replace(\".\", \",\").strip().split(',') if i != \"\"]\n",
    "    return s_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a78c3fd-a05e-4f10-ad0b-82a2764343b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_index(\"3 ,\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a6bfeb72-e196-436a-ab06-5279a80fb1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['604', '234']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_index(\" 604.234\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e6a184-647d-40da-aff2-b96d860aea9c",
   "metadata": {},
   "source": [
    "Create two dicts for each period, where the key is the conference ID, and it has two values: its subject and whether it's scientific or reformist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9178f80-5dc6-4e94-8392-8f04396a5c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(IVS+\"UIA_congresses_index.csv\", \"r\") as f: \n",
    "    reader = csv.reader(f)\n",
    "    next(reader)  # ignore field names\n",
    "    til99_is = {}\n",
    "    til14_is = {}\n",
    "    for row in reader:\n",
    "        heading = row[0]\n",
    "        is_til99 = clean_index(row[1])\n",
    "        is_til14 = clean_index(row[2])\n",
    "        sci = row[3]\n",
    "        for conference_i in is_til99:\n",
    "            if conference_i not in til99_is:\n",
    "                til99_is[conference_i] = [[], None]\n",
    "            til99_is[conference_i][0].append(heading)\n",
    "            til99_is[conference_i][1] = sci\n",
    "        for conference_i in is_til14:\n",
    "            if conference_i not in til14_is:   \n",
    "                til14_is[conference_i] = [[], None]\n",
    "            til14_is[conference_i][0].append(heading)\n",
    "            til14_is[conference_i][1] = sci       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13d16560-b5db-4f45-befc-04127c3469a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lookup_subject(s, dct):\n",
    "    \"\"\" \n",
    "    Takes a str of the conf id and the index dict,\n",
    "    Returns a string of its subjects.\n",
    "    \"\"\"\n",
    "    if s in dct: \n",
    "        return \" \".join(dct[s][0])\n",
    "    else:\n",
    "        return None\n",
    "    \n",
    "def lookup_indicator(s, dct):\n",
    "    \"\"\"\n",
    "    Takes a string of the cong id and the index dict,\n",
    "    Returns int of indicating if scientific `1` or reformist `0`\n",
    "    \"\"\"\n",
    "    if s in dct: \n",
    "        return dct[s][1]\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57892343-b7f2-4725-8f6b-05f3449a581c",
   "metadata": {},
   "outputs": [],
   "source": [
    "congresses99['subject'] = congresses99['id'].apply(lambda x: lookup_subject(x, til99_is))\n",
    "congresses99['scientific_reformist'] = congresses99['id'].apply(lambda x: lookup_indicator(x, til99_is))\n",
    "\n",
    "congresses14['subject'] = congresses14['id'].apply(lambda x: lookup_subject(x, til14_is))\n",
    "congresses14['scientific_reformist'] = congresses14['id'].apply(lambda x: lookup_indicator(x, til14_is))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1dd70d2b-3133-4765-9f8d-b3643799260e",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_congresses = pd.concat([congresses99, congresses14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb171ba4-f673-4ef9-98c0-3b7091649e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>congress</th>\n",
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       "      <th>city</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1815</td>\n",
       "      <td>2 Cng des sciences physiques et naturelles Genève</td>\n",
       "      <td>2</td>\n",
       "      <td>Genève</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1838</td>\n",
       "      <td>3 Cng celtique I Abergavenny</td>\n",
       "      <td>3</td>\n",
       "      <td>Abergavenny</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1839</td>\n",
       "      <td>4  Réunion des naturalistes scandi naves Goten...</td>\n",
       "      <td>4</td>\n",
       "      <td>Gotenburg</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1840</td>\n",
       "      <td>5 Réunion des naturalistes scandi naves Copenh...</td>\n",
       "      <td>5</td>\n",
       "      <td>Copenhague</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1840</td>\n",
       "      <td>6 Convention antiesclavagiste mnd Londres</td>\n",
       "      <td>6</td>\n",
       "      <td>Londres</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Slavery (Anti)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>1914</td>\n",
       "      <td>2440 Ass des chefs des offices hydro graph. de...</td>\n",
       "      <td>2440</td>\n",
       "      <td>Berne</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>1914</td>\n",
       "      <td>2441 Cng I des étudiants de PAmeérique du Sud ...</td>\n",
       "      <td>2441</td>\n",
       "      <td>Santiag</td>\n",
       "      <td>None</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>1914</td>\n",
       "      <td>2442 &lt;nTH&gt; Cng I espérantiste Paris</td>\n",
       "      <td>2442</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Esperantists</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>1914</td>\n",
       "      <td>2443 &lt;nTH&gt; Cng des médecins aliénistes et neur...</td>\n",
       "      <td>2443</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>1914</td>\n",
       "      <td>2445 &lt;nTH&gt; Cng I dentaire Londres</td>\n",
       "      <td>2445</td>\n",
       "      <td>Londres</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3697 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                                           congress    id  \\\n",
       "0     1815  2 Cng des sciences physiques et naturelles Genève     2   \n",
       "1     1838                       3 Cng celtique I Abergavenny     3   \n",
       "2     1839  4  Réunion des naturalistes scandi naves Goten...     4   \n",
       "3     1840  5 Réunion des naturalistes scandi naves Copenh...     5   \n",
       "4     1840          6 Convention antiesclavagiste mnd Londres     6   \n",
       "...    ...                                                ...   ...   \n",
       "2283  1914  2440 Ass des chefs des offices hydro graph. de...  2440   \n",
       "2284  1914  2441 Cng I des étudiants de PAmeérique du Sud ...  2441   \n",
       "2285  1914                2442 <nTH> Cng I espérantiste Paris  2442   \n",
       "2286  1914  2443 <nTH> Cng des médecins aliénistes et neur...  2443   \n",
       "2287  1914                  2445 <nTH> Cng I dentaire Londres  2445   \n",
       "\n",
       "             city           state         subject scientific_reformist  \n",
       "0          Genève     Switzerland            None                 None  \n",
       "1     Abergavenny  United Kingdom            None                 None  \n",
       "2       Gotenburg          Sweden            None                 None  \n",
       "3      Copenhague         Denmark            None                 None  \n",
       "4         Londres  United Kingdom  Slavery (Anti)                    0  \n",
       "...           ...             ...             ...                  ...  \n",
       "2283        Berne     Switzerland            None                 None  \n",
       "2284      Santiag            None       Students                     1  \n",
       "2285        Paris          France    Esperantists                    0  \n",
       "2286   Luxembourg      Luxembourg            None                 None  \n",
       "2287      Londres  United Kingdom            None                 None  \n",
       "\n",
       "[3697 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_congresses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f89e0b-cd6a-4d54-be01-79ec44d12465",
   "metadata": {},
   "source": [
    "Select only scientific / reformist congresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb93d141-40e2-45bc-afe1-8562c421359f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>1840</td>\n",
       "      <td>6 Convention antiesclavagiste mnd Londres</td>\n",
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       "      <td>Londres</td>\n",
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       "      <th>6</th>\n",
       "      <td>1843</td>\n",
       "      <td>8 Convention antiesclavagiste mnd Londres</td>\n",
       "      <td>8</td>\n",
       "      <td>Londres</td>\n",
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       "      <th>7</th>\n",
       "      <td>1843</td>\n",
       "      <td>9 Cnf scandinave d’étudiants Uppsala</td>\n",
       "      <td>9</td>\n",
       "      <td>Uppsala</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Students</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1843</td>\n",
       "      <td>10 Cng I de la Paix Londres</td>\n",
       "      <td>10</td>\n",
       "      <td>Londres</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Peace</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1845</td>\n",
       "      <td>12 Cnf scandinave d’étudiants Copenhague</td>\n",
       "      <td>12</td>\n",
       "      <td>Copenhague</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>1914</td>\n",
       "      <td>2418 &lt;nTH&gt; Cnf I de la Fed abolitionniste Port...</td>\n",
       "      <td>2418</td>\n",
       "      <td>Portsmouth</td>\n",
       "      <td>None</td>\n",
       "      <td>Abolitionism</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>1914</td>\n",
       "      <td>2423 &lt;nTH&gt; Cnf triennale pour les aveugles Lon...</td>\n",
       "      <td>2423</td>\n",
       "      <td>Londres</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Blind Persons</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>1914</td>\n",
       "      <td>2426 Ass Gén de l’Asn Gén des ingé nieurs, arc...</td>\n",
       "      <td>2426</td>\n",
       "      <td>Lyon</td>\n",
       "      <td>France</td>\n",
       "      <td>Health, Municipal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>1914</td>\n",
       "      <td>2441 Cng I des étudiants de PAmeérique du Sud ...</td>\n",
       "      <td>2441</td>\n",
       "      <td>Santiag</td>\n",
       "      <td>None</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>1914</td>\n",
       "      <td>2442 &lt;nTH&gt; Cng I espérantiste Paris</td>\n",
       "      <td>2442</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Esperantists</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1060 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                                           congress    id  \\\n",
       "4     1840          6 Convention antiesclavagiste mnd Londres     6   \n",
       "6     1843          8 Convention antiesclavagiste mnd Londres     8   \n",
       "7     1843               9 Cnf scandinave d’étudiants Uppsala     9   \n",
       "8     1843                        10 Cng I de la Paix Londres    10   \n",
       "10    1845           12 Cnf scandinave d’étudiants Copenhague    12   \n",
       "...    ...                                                ...   ...   \n",
       "2265  1914  2418 <nTH> Cnf I de la Fed abolitionniste Port...  2418   \n",
       "2269  1914  2423 <nTH> Cnf triennale pour les aveugles Lon...  2423   \n",
       "2272  1914  2426 Ass Gén de l’Asn Gén des ingé nieurs, arc...  2426   \n",
       "2284  1914  2441 Cng I des étudiants de PAmeérique du Sud ...  2441   \n",
       "2285  1914                2442 <nTH> Cng I espérantiste Paris  2442   \n",
       "\n",
       "            city           state            subject scientific_reformist  \n",
       "4        Londres  United Kingdom     Slavery (Anti)                    0  \n",
       "6        Londres  United Kingdom     Slavery (Anti)                    0  \n",
       "7        Uppsala          Sweden          Students                     1  \n",
       "8        Londres  United Kingdom              Peace                    0  \n",
       "10    Copenhague         Denmark          Students                     1  \n",
       "...          ...             ...                ...                  ...  \n",
       "2265  Portsmouth            None       Abolitionism                    0  \n",
       "2269     Londres  United Kingdom      Blind Persons                    0  \n",
       "2272        Lyon          France  Health, Municipal                    1  \n",
       "2284     Santiag            None          Students                     1  \n",
       "2285       Paris          France       Esperantists                    0  \n",
       "\n",
       "[1060 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_ref_congresses = all_congresses[(all_congresses['scientific_reformist'] == \"1\") | (all_congresses['scientific_reformist'] == \"0\")]\n",
    "sci_ref_congresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a2686ce8-bb2a-4ba5-8de9-035587a1f174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2867189613199892"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_ref_congresses.shape[0] / all_congresses.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "91066673-7b70-440e-ac19-decf73e0ab8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>congress</th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>subject</th>\n",
       "      <th>scientific_reformist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1843</td>\n",
       "      <td>9 Cnf scandinave d’étudiants Uppsala</td>\n",
       "      <td>9</td>\n",
       "      <td>Uppsala</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1845</td>\n",
       "      <td>12 Cnf scandinave d’étudiants Copenhague</td>\n",
       "      <td>12</td>\n",
       "      <td>Copenhague</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1847</td>\n",
       "      <td>18 Cng I des économistes Bruxelles</td>\n",
       "      <td>18</td>\n",
       "      <td>Bruxelles</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Economists</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1853</td>\n",
       "      <td>32 Cng général de statistique Bruxelles</td>\n",
       "      <td>32</td>\n",
       "      <td>Bruxelles</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1855</td>\n",
       "      <td>36 Cng I de statistique Paris</td>\n",
       "      <td>36</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>1913</td>\n",
       "      <td>2382 &lt;nTH&gt; Cng I du bureau I des fed nat du pe...</td>\n",
       "      <td>2382</td>\n",
       "      <td>Gand</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Teachers Teaching</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>1914</td>\n",
       "      <td>2391 &lt;nTH&gt; Cng Cmsn I de l’enseignement mathém...</td>\n",
       "      <td>2391</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Teaching</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>1914</td>\n",
       "      <td>2407 Cng I d’ethnologie et d’ethnogra phie Neu...</td>\n",
       "      <td>2407</td>\n",
       "      <td>Neuchatel</td>\n",
       "      <td>None</td>\n",
       "      <td>Ethnography Ethnology</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>1914</td>\n",
       "      <td>2426 Ass Gén de l’Asn Gén des ingé nieurs, arc...</td>\n",
       "      <td>2426</td>\n",
       "      <td>Lyon</td>\n",
       "      <td>France</td>\n",
       "      <td>Health, Municipal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>1914</td>\n",
       "      <td>2441 Cng I des étudiants de PAmeérique du Sud ...</td>\n",
       "      <td>2441</td>\n",
       "      <td>Santiag</td>\n",
       "      <td>None</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                                           congress    id  \\\n",
       "7     1843               9 Cnf scandinave d’étudiants Uppsala     9   \n",
       "10    1845           12 Cnf scandinave d’étudiants Copenhague    12   \n",
       "15    1847                 18 Cng I des économistes Bruxelles    18   \n",
       "28    1853            32 Cng général de statistique Bruxelles    32   \n",
       "32    1855                      36 Cng I de statistique Paris    36   \n",
       "...    ...                                                ...   ...   \n",
       "2232  1913  2382 <nTH> Cng I du bureau I des fed nat du pe...  2382   \n",
       "2239  1914  2391 <nTH> Cng Cmsn I de l’enseignement mathém...  2391   \n",
       "2255  1914  2407 Cng I d’ethnologie et d’ethnogra phie Neu...  2407   \n",
       "2272  1914  2426 Ass Gén de l’Asn Gén des ingé nieurs, arc...  2426   \n",
       "2284  1914  2441 Cng I des étudiants de PAmeérique du Sud ...  2441   \n",
       "\n",
       "            city    state                subject scientific_reformist  \n",
       "7        Uppsala   Sweden              Students                     1  \n",
       "10    Copenhague  Denmark              Students                     1  \n",
       "15     Bruxelles  Belgium             Economists                    1  \n",
       "28     Bruxelles  Belgium             Statistics                    1  \n",
       "32         Paris   France             Statistics                    1  \n",
       "...          ...      ...                    ...                  ...  \n",
       "2232        Gand  Belgium      Teachers Teaching                    1  \n",
       "2239       Paris   France               Teaching                    1  \n",
       "2255   Neuchatel     None  Ethnography Ethnology                    1  \n",
       "2272        Lyon   France      Health, Municipal                    1  \n",
       "2284     Santiag     None              Students                     1  \n",
       "\n",
       "[498 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_congresses = all_congresses[(all_congresses['scientific_reformist'] == \"1\")]\n",
    "sci_congresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f8985ec6-030b-4f5d-ad09-ae66a32ed1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.469811320754717"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_congresses.shape[0] / sci_ref_congresses.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5bab53f7-02d2-484b-a649-c0f18d903d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13470381390316472"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_congresses.shape[0] / all_congresses.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac77d9b5-1a83-4681-b2ef-7ec98f2a9388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 624,
       "width": 813
      },
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title_color, tick_color, edge_color, data_color = 'black', 'darkgray', 'dimgray', 'gainsboro'\n",
    "font_size = 25\n",
    "font_weight = \"bold\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.style.use('seaborn-white')\n",
    "plt.setp(ax.spines.values(), color=data_color)\n",
    "\n",
    "plt.hist(sorted(all_congresses['year']), \n",
    "         bins=115,\n",
    "         range=[1800,1920],\n",
    "         edgecolor=edge_color, \n",
    "         facecolor=data_color, \n",
    "         linewidth=1.5)\n",
    "\n",
    "\n",
    "plt.plot(all_congresses.groupby('year').count().index, \n",
    "         all_congresses.groupby('year').count()['congress'],  \n",
    "         linewidth=2.5,\n",
    "         color=tick_color, scaley=False)\n",
    "\n",
    "plt.plot(sci_ref_congresses.groupby('year').count().index, \n",
    "         sci_ref_congresses.groupby('year').count()['congress'],  \n",
    "         linewidth=2.5, \n",
    "         color=edge_color)\n",
    "\n",
    "plt.plot(sci_congresses.groupby('year').count().index, \n",
    "         sci_congresses.groupby('year').count()['congress'],  \n",
    "         linewidth=2.5, \n",
    "         color=title_color)\n",
    "\n",
    "# plt.xlabel('N Tokens per Review Comment', \n",
    "#            fontsize=font_size, \n",
    "#            labelpad=25, \n",
    "#            color=title_color, \n",
    "#            weight=font_weight)\n",
    "\n",
    "plt.ylabel('N Congresses/\\nConferences', \n",
    "           fontsize=font_size, \n",
    "           rotation=0,\n",
    "           labelpad=105, \n",
    "           color=title_color, \n",
    "           weight=font_weight)\n",
    "\n",
    "# plt.grid(color=tick_color, \n",
    "#          axis=\"y\", \n",
    "#          alpha=0.8, \n",
    "#          linestyle=':', \n",
    "#          linewidth=1)\n",
    "\n",
    "plt.xticks(ticks=list(range(1800, 1921, 30)),\n",
    "           fontsize=25, \n",
    "           color=tick_color, \n",
    "           weight=font_weight,\n",
    "           rotation = 60)\n",
    "\n",
    "plt.yticks(ticks=list(range(min(all_congresses.groupby('year').count()['congress'])-1, \n",
    "                            max(all_congresses.groupby('year').count()['congress']), 50)),\n",
    "           fontsize=25, \n",
    "           color=tick_color, \n",
    "           weight=font_weight)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37dec34f-c8de-4101-81c3-33391564f0ac",
   "metadata": {},
   "source": [
    "# Convert to country-year dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08423c87-0f24-4c38-a0a6-240aa0439f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_years = {}\n",
    "for country, country_df in all_congresses.groupby('state'):\n",
    "    if country not in country_years: \n",
    "        country_years[country] = {}\n",
    "    for year, country_year_df in country_df.groupby(\"year\"):\n",
    "        if year not in country_years[country]:\n",
    "            country_years[country][year] = []\n",
    "        yr_total_cong = country_year_df.shape[0]\n",
    "        yr_sci_ref_cong = country_year_df[(country_year_df['scientific_reformist'] == \"1\") | \n",
    "                                  (country_year_df['scientific_reformist'] == \"0\")].shape[0]\n",
    "        sci_cong = country_year_df[(country_year_df['scientific_reformist'] == \"1\")].shape[0]\n",
    "        country_years[country][year].extend([yr_total_cong, yr_sci_ref_cong, sci_cong])\n",
    "\n",
    "with open(IVS+\"19c_congresses_cy.csv\", 'w') as f: \n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([\"year\", \"state\", \"n_confs_ttl\", \"n_confs_sci_ref\", \"n_confs_sci\"])\n",
    "    for year in range(1800, 1914, 1):\n",
    "        for country, years in country_years.items():\n",
    "            if year in years: \n",
    "                row = [year, country, years[year][0], years[year][1], years[year][2]]\n",
    "                writer.writerow(row)\n",
    "            else:\n",
    "                row = [year, country, 0, 0, 0]\n",
    "                writer.writerow(row)\n",
    "                \n",
    "                \n",
    "pd.read_csv(IVS+\"19c_congresses_cy.csv\").to_stata(IVS+\"19c_congresses_cy.dta\", write_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b7462955-1bdd-46f8-920b-843f10bdd765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>congress</th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>subject</th>\n",
       "      <th>scientific_reformist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1843</td>\n",
       "      <td>9 Cnf scandinave d’étudiants Uppsala</td>\n",
       "      <td>9</td>\n",
       "      <td>Uppsala</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1845</td>\n",
       "      <td>12 Cnf scandinave d’étudiants Copenhague</td>\n",
       "      <td>12</td>\n",
       "      <td>Copenhague</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Students</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1847</td>\n",
       "      <td>18 Cng I des économistes Bruxelles</td>\n",
       "      <td>18</td>\n",
       "      <td>Bruxelles</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Economists</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1853</td>\n",
       "      <td>32 Cng général de statistique Bruxelles</td>\n",
       "      <td>32</td>\n",
       "      <td>Bruxelles</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1855</td>\n",
       "      <td>36 Cng I de statistique Paris</td>\n",
       "      <td>36</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Statistics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year                                  congress  id        city    state  \\\n",
       "7   1843      9 Cnf scandinave d’étudiants Uppsala   9     Uppsala   Sweden   \n",
       "10  1845  12 Cnf scandinave d’étudiants Copenhague  12  Copenhague  Denmark   \n",
       "15  1847        18 Cng I des économistes Bruxelles  18   Bruxelles  Belgium   \n",
       "28  1853   32 Cng général de statistique Bruxelles  32   Bruxelles  Belgium   \n",
       "32  1855             36 Cng I de statistique Paris  36       Paris   France   \n",
       "\n",
       "       subject scientific_reformist  \n",
       "7    Students                     1  \n",
       "10   Students                     1  \n",
       "15  Economists                    1  \n",
       "28  Statistics                    1  \n",
       "32  Statistics                    1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_congresses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f120b7b1-9d59-412c-b395-8343724a5a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(IVS+\"19c_congresses_yr.csv\", \"w\") as f: \n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([\"year\", \"yr_n_cnfs_ttl\", \"yr_n_cnfs_sci_ref\", \"yr_n_cnfs_sci\"])\n",
    "    for year, year_df in all_congresses.groupby(\"year\"):\n",
    "        n_confs_ttl = year_df.shape[0]\n",
    "        n_cnfs_sci_ref = year_df[year_df['scientific_reformist'] == \"0\"].shape[0]\n",
    "        n_cnfs_sci = year_df[year_df['scientific_reformist'] == \"1\"].shape[0]\n",
    "        writer.writerow([year, n_confs_ttl, n_cnfs_sci_ref, n_cnfs_sci])\n",
    "        \n",
    "pd.read_csv(IVS+\"19c_congresses_yr.csv\").to_stata(IVS+\"19c_congresses_yr.dta\", write_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "279feb15-26fe-4d95-bd7f-2ee75f21e070",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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